Both our internal Social Networks Analysis community and Colleen Haikes (IBM External Relations) tipped me off to some absolutely fascinating research on the quantitative correlation between social networks and performance based on an analysis of IBM consultants. You can read the research summary and view the presentation, or read the research paper for all the details. Highlights and what I think about them:

Structurally diverse networks with abundance of structural holes are associated with higher performance. Having diverse friends helps. The presentation gives more detail – it’s not about having a diverse personal network, but it’s about connecting to people who also have diverse networks. I suspect this is related to having connectors in your network.

Betweenness is negatively correlated. Being a bridge between a lot of people is not helpful. The presentation clarified this by saying that the optimal team composition is not a team of connected superstars, but complementary team members with a few well-connected information keepers.

Strong ties are positively correlated with performance for pre-sales teams, but negatively correlated with performance for consultants. Pre-sales teams need to build relationships, while consultants often need to solve a wide variety of challenges.

Look! Actual dollar values and significant differences! Wow. =)

Here’s another piece of research the totally awesome IBM researchers put together:

A separate IBM study, presented at the CHI conference in Boston this week, sheds light on why it’s easier said than done to add new, potentially valuable contacts to one’s social network in the workplace. The study looked at several types of automated “friend-recommender” engines on social networking sites. The recommender engines used algorithms that identified potential contacts based on common friends, common interests, and common hyperlinks listed on someone’s profile.

Although most people using social media for the workplace claimed to be open to finding previously unknown friends, they were most comfortable with the recommender engines that suggested “friends’ friends” — generally, people whom they already knew of. The friend-recommenders with the lowest acceptance rates were those that merely look at whether people have similar interests — although they were the most effective at identifying completely new, potentially valuable contacts. Friend-recommenders that took the greatest factors into account were deemed the most useful. (IBM’s Facebook-style social networking site, Beehive, uses this type of friend-recommender engine.)

Personally, I don’t use friend recommenders to connect to completely new people, but they’re great for reminding me about people I already know.